Overview

Dataset statistics

Number of variables13
Number of observations5967
Missing cells5106
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory1.1 KiB

Variable types

Text9
Numeric1
Categorical2
DateTime1

Alerts

Director has 2064 (34.6%) missing valuesMissing
Cast has 530 (8.9%) missing valuesMissing
Production Country has 559 (9.4%) missing valuesMissing
Imdb Score has 608 (10.2%) missing valuesMissing
Date Added has 1335 (22.4%) missing valuesMissing
Show Id has unique valuesUnique

Reproduction

Analysis started2023-12-16 02:29:54.027738
Analysis finished2023-12-16 02:29:59.798954
Duration5.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Show Id
Text

UNIQUE 

Distinct5967
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size542.0 KiB
2023-12-16T05:30:00.355920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters214812
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5967 ?
Unique (%)100.0%

Sample

1st rowcc1b6ed9-cf9e-4057-8303-34577fb54477
2nd rowe2ef4e91-fb25-42ab-b485-be8e3b23dedb
3rd rowb01b73b7-81f6-47a7-86d8-acb63080d525
4th rowb6611af0-f53c-4a08-9ffa-9716dc57eb9c
5th row7f2d4170-bab8-4d75-adc2-197f7124c070
ValueCountFrequency (%)
cc1b6ed9-cf9e-4057-8303-34577fb54477 1
 
< 0.1%
ea94d1e2-dfb5-4fb0-b941-ca4b1ade98c1 1
 
< 0.1%
b01b73b7-81f6-47a7-86d8-acb63080d525 1
 
< 0.1%
b6611af0-f53c-4a08-9ffa-9716dc57eb9c 1
 
< 0.1%
7f2d4170-bab8-4d75-adc2-197f7124c070 1
 
< 0.1%
c293788a-41f7-49a3-a7fc-005ea33bce2b 1
 
< 0.1%
0555e67e-f624-4a05-93e4-55c117d0056d 1
 
< 0.1%
c844460f-6178-4f87-929e-80816c74ca35 1
 
< 0.1%
8b34e0e9-7258-4e49-b799-2e7eddbd7e34 1
 
< 0.1%
6da2fc83-1546-4e9d-bf2e-9b472a059c18 1
 
< 0.1%
Other values (5957) 5957
99.8%
2023-12-16T05:30:01.043881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 23868
 
11.1%
4 17112
 
8.0%
8 12991
 
6.0%
a 12780
 
5.9%
9 12692
 
5.9%
b 12656
 
5.9%
5 11340
 
5.3%
0 11305
 
5.3%
d 11246
 
5.2%
7 11225
 
5.2%
Other values (7) 77597
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121043
56.3%
Lowercase Letter 69901
32.5%
Dash Punctuation 23868
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 17112
14.1%
8 12991
10.7%
9 12692
10.5%
5 11340
9.4%
0 11305
9.3%
7 11225
9.3%
3 11173
9.2%
2 11140
9.2%
1 11096
9.2%
6 10969
9.1%
Lowercase Letter
ValueCountFrequency (%)
a 12780
18.3%
b 12656
18.1%
d 11246
16.1%
e 11174
16.0%
c 11044
15.8%
f 11001
15.7%
Dash Punctuation
ValueCountFrequency (%)
- 23868
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 144911
67.5%
Latin 69901
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 23868
16.5%
4 17112
11.8%
8 12991
9.0%
9 12692
8.8%
5 11340
7.8%
0 11305
7.8%
7 11225
7.7%
3 11173
7.7%
2 11140
7.7%
1 11096
7.7%
Latin
ValueCountFrequency (%)
a 12780
18.3%
b 12656
18.1%
d 11246
16.1%
e 11174
16.0%
c 11044
15.8%
f 11001
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 214812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 23868
 
11.1%
4 17112
 
8.0%
8 12991
 
6.0%
a 12780
 
5.9%
9 12692
 
5.9%
b 12656
 
5.9%
5 11340
 
5.3%
0 11305
 
5.3%
d 11246
 
5.2%
7 11225
 
5.2%
Other values (7) 77597
36.1%

Title
Text

Distinct5897
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size446.7 KiB
2023-12-16T05:30:01.406174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length104
Median length69
Mean length17.808446
Min length1

Characters and Unicode

Total characters106263
Distinct characters164
Distinct categories15 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5832 ?
Unique (%)97.7%

Sample

1st row(Un)Well
2nd row#Alive
3rd row#AnneFrank - Parallel Stories
4th row#blackAF
5th row#cats_the_mewvie
ValueCountFrequency (%)
the 1471
 
7.9%
of 442
 
2.4%
a 241
 
1.3%
in 208
 
1.1%
195
 
1.0%
love 135
 
0.7%
and 133
 
0.7%
to 130
 
0.7%
my 105
 
0.6%
2 91
 
0.5%
Other values (6900) 15449
83.1%
2023-12-16T05:30:02.001289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12627
 
11.9%
e 9903
 
9.3%
a 7455
 
7.0%
o 5964
 
5.6%
i 5906
 
5.6%
r 5550
 
5.2%
n 5520
 
5.2%
t 4764
 
4.5%
s 4247
 
4.0%
l 3613
 
3.4%
Other values (154) 40714
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73610
69.3%
Uppercase Letter 17185
 
16.2%
Space Separator 12636
 
11.9%
Other Punctuation 1929
 
1.8%
Decimal Number 548
 
0.5%
Dash Punctuation 152
 
0.1%
Open Punctuation 51
 
< 0.1%
Close Punctuation 50
 
< 0.1%
Other Letter 43
 
< 0.1%
Final Punctuation 35
 
< 0.1%
Other values (5) 24
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9903
13.5%
a 7455
10.1%
o 5964
 
8.1%
i 5906
 
8.0%
r 5550
 
7.5%
n 5520
 
7.5%
t 4764
 
6.5%
s 4247
 
5.8%
l 3613
 
4.9%
h 3588
 
4.9%
Other values (42) 17100
23.2%
Other Letter
ValueCountFrequency (%)
ا 3
 
7.0%
ر 2
 
4.7%
ل 2
 
4.7%
ة 2
 
4.7%
ف 2
 
4.7%
웅 1
 
2.3%
영 1
 
2.3%
대 1
 
2.3%
탄 1
 
2.3%
공 1
 
2.3%
Other values (27) 27
62.8%
Uppercase Letter
ValueCountFrequency (%)
T 1872
 
10.9%
S 1540
 
9.0%
M 1286
 
7.5%
B 1107
 
6.4%
A 1065
 
6.2%
C 1026
 
6.0%
L 901
 
5.2%
D 829
 
4.8%
H 752
 
4.4%
P 696
 
4.1%
Other values (23) 6111
35.6%
Other Punctuation
ValueCountFrequency (%)
: 1057
54.8%
' 264
 
13.7%
. 164
 
8.5%
& 125
 
6.5%
, 108
 
5.6%
! 106
 
5.5%
? 44
 
2.3%
* 25
 
1.3%
/ 15
 
0.8%
# 10
 
0.5%
Other values (5) 11
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 160
29.2%
0 84
15.3%
1 81
14.8%
9 53
 
9.7%
3 48
 
8.8%
4 30
 
5.5%
8 25
 
4.6%
6 24
 
4.4%
5 22
 
4.0%
7 21
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 4
50.0%
~ 2
25.0%
| 2
25.0%
Space Separator
ValueCountFrequency (%)
12627
99.9%
  9
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 150
98.7%
– 2
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 50
98.0%
„ 1
 
2.0%
Final Punctuation
ValueCountFrequency (%)
’ 32
91.4%
” 3
 
8.6%
Initial Punctuation
ValueCountFrequency (%)
“ 2
66.7%
‘ 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Format
ValueCountFrequency (%)
​ 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Other Number
ValueCountFrequency (%)
² 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90795
85.4%
Common 15425
 
14.5%
Hangul 20
 
< 0.1%
Arabic 19
 
< 0.1%
Han 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9903
 
10.9%
a 7455
 
8.2%
o 5964
 
6.6%
i 5906
 
6.5%
r 5550
 
6.1%
n 5520
 
6.1%
t 4764
 
5.2%
s 4247
 
4.7%
l 3613
 
4.0%
h 3588
 
4.0%
Other values (75) 34285
37.8%
Common
ValueCountFrequency (%)
12627
81.9%
: 1057
 
6.9%
' 264
 
1.7%
. 164
 
1.1%
2 160
 
1.0%
- 150
 
1.0%
& 125
 
0.8%
, 108
 
0.7%
! 106
 
0.7%
0 84
 
0.5%
Other values (32) 580
 
3.8%
Hangul
ValueCountFrequency (%)
웅 1
 
5.0%
영 1
 
5.0%
대 1
 
5.0%
탄 1
 
5.0%
공 1
 
5.0%
특 1
 
5.0%
니 1
 
5.0%
미 1
 
5.0%
사 1
 
5.0%
전 1
 
5.0%
Other values (10) 10
50.0%
Arabic
ValueCountFrequency (%)
ا 3
15.8%
ر 2
10.5%
ل 2
10.5%
ة 2
10.5%
ف 2
10.5%
د 1
 
5.3%
ع 1
 
5.3%
ق 1
 
5.3%
ه 1
 
5.3%
م 1
 
5.3%
Other values (3) 3
15.8%
Han
ValueCountFrequency (%)
子 1
25.0%
海 1
25.0%
的 1
25.0%
儿 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105994
99.7%
None 175
 
0.2%
Punctuation 51
 
< 0.1%
Hangul 20
 
< 0.1%
Arabic 19
 
< 0.1%
CJK 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12627
 
11.9%
e 9903
 
9.3%
a 7455
 
7.0%
o 5964
 
5.6%
i 5906
 
5.6%
r 5550
 
5.2%
n 5520
 
5.2%
t 4764
 
4.5%
s 4247
 
4.0%
l 3613
 
3.4%
Other values (73) 40445
38.2%
Punctuation
ValueCountFrequency (%)
’ 32
62.7%
​ 8
 
15.7%
” 3
 
5.9%
… 2
 
3.9%
– 2
 
3.9%
“ 2
 
3.9%
„ 1
 
2.0%
‘ 1
 
2.0%
None
ValueCountFrequency (%)
é 29
16.6%
í 21
12.0%
ó 16
 
9.1%
á 16
 
9.1%
ñ 15
 
8.6%
ü 14
 
8.0%
  9
 
5.1%
ı 7
 
4.0%
ł 5
 
2.9%
ô 3
 
1.7%
Other values (26) 40
22.9%
Arabic
ValueCountFrequency (%)
ا 3
15.8%
ر 2
10.5%
ل 2
10.5%
ة 2
10.5%
ف 2
10.5%
د 1
 
5.3%
ع 1
 
5.3%
ق 1
 
5.3%
ه 1
 
5.3%
م 1
 
5.3%
Other values (3) 3
15.8%
Hangul
ValueCountFrequency (%)
웅 1
 
5.0%
영 1
 
5.0%
대 1
 
5.0%
탄 1
 
5.0%
공 1
 
5.0%
특 1
 
5.0%
니 1
 
5.0%
미 1
 
5.0%
사 1
 
5.0%
전 1
 
5.0%
Other values (10) 10
50.0%
CJK
ValueCountFrequency (%)
子 1
25.0%
海 1
25.0%
的 1
25.0%
儿 1
25.0%

Description
Text

Distinct5946
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2023-12-16T05:30:02.483279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length248
Median length218
Mean length143.57935
Min length61

Characters and Unicode

Total characters856738
Distinct characters124
Distinct categories15 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5929 ?
Unique (%)99.4%

Sample

1st rowThis docuseries takes a deep dive into the lucrative wellness industry, which touts health and healing. But do the products live up to the promises?
2nd rowAs a grisly virus rampages a city, a lone man stays locked inside his apartment, digitally cut off from seeking help and desperate to find a way out.
3rd rowThrough her diary, Anne Frank's story is retold alongside those of five Holocaust survivors in this poignant documentary from Oscar winner Helen Mirren.
4th rowKenya Barris and his family navigate relationships, race and culture while grappling with their newfound success in this comedy series.
5th rowThis pawesome documentary explores how our feline friends became online icons, from the earliest text memes to the rise of celebrity cat influencers.
ValueCountFrequency (%)
a 7881
 
5.5%
the 5400
 
3.8%
and 4456
 
3.1%
to 4313
 
3.0%
of 3513
 
2.5%
in 2999
 
2.1%
his 2289
 
1.6%
with 1593
 
1.1%
her 1560
 
1.1%
an 1337
 
0.9%
Other values (17344) 107549
75.3%
2023-12-16T05:30:03.140054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
136917
16.0%
e 80579
 
9.4%
a 57660
 
6.7%
t 54775
 
6.4%
i 53313
 
6.2%
n 50571
 
5.9%
s 49516
 
5.8%
o 48981
 
5.7%
r 48173
 
5.6%
h 32798
 
3.8%
Other values (114) 243455
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 681021
79.5%
Space Separator 136926
 
16.0%
Uppercase Letter 17772
 
2.1%
Other Punctuation 15334
 
1.8%
Dash Punctuation 3133
 
0.4%
Decimal Number 2056
 
0.2%
Final Punctuation 391
 
< 0.1%
Initial Punctuation 33
 
< 0.1%
Close Punctuation 22
 
< 0.1%
Open Punctuation 22
 
< 0.1%
Other values (5) 28
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 80579
11.8%
a 57660
 
8.5%
t 54775
 
8.0%
i 53313
 
7.8%
n 50571
 
7.4%
s 49516
 
7.3%
o 48981
 
7.2%
r 48173
 
7.1%
h 32798
 
4.8%
l 28138
 
4.1%
Other values (44) 176517
25.9%
Uppercase Letter
ValueCountFrequency (%)
A 2724
15.3%
T 1474
 
8.3%
S 1263
 
7.1%
B 1110
 
6.2%
W 1106
 
6.2%
C 1060
 
6.0%
I 1037
 
5.8%
M 933
 
5.2%
F 708
 
4.0%
D 631
 
3.6%
Other values (21) 5726
32.2%
Other Punctuation
ValueCountFrequency (%)
. 6784
44.2%
, 6094
39.7%
' 1666
 
10.9%
" 502
 
3.3%
: 100
 
0.7%
! 97
 
0.6%
? 67
 
0.4%
/ 9
 
0.1%
& 6
 
< 0.1%
; 4
 
< 0.1%
Other values (3) 5
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 497
24.2%
1 480
23.3%
9 295
14.3%
2 208
10.1%
8 115
 
5.6%
5 98
 
4.8%
7 97
 
4.7%
3 91
 
4.4%
4 88
 
4.3%
6 87
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 2465
78.7%
– 427
 
13.6%
— 241
 
7.7%
Space Separator
ValueCountFrequency (%)
136917
> 99.9%
  9
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
’ 355
90.8%
” 36
 
9.2%
Initial Punctuation
ValueCountFrequency (%)
“ 32
97.0%
‘ 1
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 20
100.0%
Other Letter
ValueCountFrequency (%)
º 5
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%
Nonspacing Mark
ValueCountFrequency (%)
́ 1
100.0%
Other Symbol
ValueCountFrequency (%)
® 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 698798
81.6%
Common 157939
 
18.4%
Inherited 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 80579
11.5%
a 57660
 
8.3%
t 54775
 
7.8%
i 53313
 
7.6%
n 50571
 
7.2%
s 49516
 
7.1%
o 48981
 
7.0%
r 48173
 
6.9%
h 32798
 
4.7%
l 28138
 
4.0%
Other values (76) 194294
27.8%
Common
ValueCountFrequency (%)
136917
86.7%
. 6784
 
4.3%
, 6094
 
3.9%
- 2465
 
1.6%
' 1666
 
1.1%
" 502
 
0.3%
0 497
 
0.3%
1 480
 
0.3%
– 427
 
0.3%
’ 355
 
0.2%
Other values (27) 1752
 
1.1%
Inherited
ValueCountFrequency (%)
́ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 855383
99.8%
Punctuation 1094
 
0.1%
None 259
 
< 0.1%
Modifier Letters 1
 
< 0.1%
Diacriticals 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
136917
16.0%
e 80579
 
9.4%
a 57660
 
6.7%
t 54775
 
6.4%
i 53313
 
6.2%
n 50571
 
5.9%
s 49516
 
5.8%
o 48981
 
5.7%
r 48173
 
5.6%
h 32798
 
3.8%
Other values (69) 242100
28.3%
Punctuation
ValueCountFrequency (%)
– 427
39.0%
’ 355
32.4%
— 241
22.0%
” 36
 
3.3%
“ 32
 
2.9%
… 2
 
0.2%
‘ 1
 
0.1%
None
ValueCountFrequency (%)
é 79
30.5%
á 44
17.0%
í 31
 
12.0%
ó 13
 
5.0%
ñ 10
 
3.9%
  9
 
3.5%
ü 7
 
2.7%
ú 6
 
2.3%
ï 6
 
2.3%
ł 6
 
2.3%
Other values (26) 48
18.5%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%
Diacriticals
ValueCountFrequency (%)
́ 1
100.0%

Director
Text

MISSING 

Distinct2993
Distinct (%)76.7%
Missing2064
Missing (%)34.6%
Memory size352.1 KiB
2023-12-16T05:30:03.650802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length208
Median length108
Mean length15.245708
Min length2

Characters and Unicode

Total characters59504
Distinct characters99
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2491 ?
Unique (%)63.8%

Sample

1st rowCho Il
2nd rowSabina Fedeli, Anna Migotto
3rd rowMichael Margolis
4th rowRako Prijanto
5th rowRako Prijanto
ValueCountFrequency (%)
michael 66
 
0.7%
david 60
 
0.6%
paul 51
 
0.6%
john 48
 
0.5%
chris 36
 
0.4%
jay 34
 
0.4%
james 30
 
0.3%
mike 30
 
0.3%
robert 28
 
0.3%
lee 27
 
0.3%
Other values (4747) 8829
95.6%
2023-12-16T05:30:04.488830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6490
 
10.9%
5336
 
9.0%
e 4279
 
7.2%
n 3954
 
6.6%
i 3729
 
6.3%
r 3387
 
5.7%
o 3215
 
5.4%
l 2382
 
4.0%
h 2085
 
3.5%
s 1939
 
3.3%
Other values (89) 22708
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43872
73.7%
Uppercase Letter 9425
 
15.8%
Space Separator 5336
 
9.0%
Other Punctuation 719
 
1.2%
Dash Punctuation 149
 
0.3%
Final Punctuation 2
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6490
14.8%
e 4279
9.8%
n 3954
 
9.0%
i 3729
 
8.5%
r 3387
 
7.7%
o 3215
 
7.3%
l 2382
 
5.4%
h 2085
 
4.8%
s 1939
 
4.4%
t 1759
 
4.0%
Other values (44) 10653
24.3%
Uppercase Letter
ValueCountFrequency (%)
S 954
 
10.1%
M 922
 
9.8%
A 774
 
8.2%
J 636
 
6.7%
R 590
 
6.3%
C 570
 
6.0%
B 549
 
5.8%
K 479
 
5.1%
D 443
 
4.7%
P 414
 
4.4%
Other values (25) 3094
32.8%
Other Punctuation
ValueCountFrequency (%)
, 481
66.9%
. 211
29.3%
' 22
 
3.1%
& 2
 
0.3%
" 2
 
0.3%
! 1
 
0.1%
Space Separator
ValueCountFrequency (%)
5336
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 149
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Decimal Number
ValueCountFrequency (%)
9 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53297
89.6%
Common 6207
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6490
 
12.2%
e 4279
 
8.0%
n 3954
 
7.4%
i 3729
 
7.0%
r 3387
 
6.4%
o 3215
 
6.0%
l 2382
 
4.5%
h 2085
 
3.9%
s 1939
 
3.6%
t 1759
 
3.3%
Other values (79) 20078
37.7%
Common
ValueCountFrequency (%)
5336
86.0%
, 481
 
7.7%
. 211
 
3.4%
- 149
 
2.4%
' 22
 
0.4%
& 2
 
< 0.1%
" 2
 
< 0.1%
’ 2
 
< 0.1%
9 1
 
< 0.1%
! 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59159
99.4%
None 343
 
0.6%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6490
 
11.0%
5336
 
9.0%
e 4279
 
7.2%
n 3954
 
6.7%
i 3729
 
6.3%
r 3387
 
5.7%
o 3215
 
5.4%
l 2382
 
4.0%
h 2085
 
3.5%
s 1939
 
3.3%
Other values (51) 22363
37.8%
None
ValueCountFrequency (%)
á 48
14.0%
é 45
13.1%
í 32
9.3%
ó 29
8.5%
ı 28
 
8.2%
ü 25
 
7.3%
ú 23
 
6.7%
ç 16
 
4.7%
ö 14
 
4.1%
ğ 12
 
3.5%
Other values (27) 71
20.7%
Punctuation
ValueCountFrequency (%)
’ 2
100.0%

Genres
Text

Distinct433
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size531.4 KiB
2023-12-16T05:30:04.847270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length79
Median length58
Mean length34.168426
Min length6

Characters and Unicode

Total characters203883
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)2.2%

Sample

1st rowReality TV
2nd rowHorror Movies, International Movies, Thrillers
3rd rowDocumentaries, International Movies
4th rowTV Comedies
5th rowDocumentaries, International Movies
ValueCountFrequency (%)
tv 4399
16.0%
movies 3634
13.2%
international 3029
11.0%
shows 2318
 
8.4%
dramas 2204
 
8.0%
1635
 
6.0%
comedies 1578
 
5.7%
romantic 718
 
2.6%
action 655
 
2.4%
adventure 655
 
2.4%
Other values (33) 6631
24.2%
2023-12-16T05:30:05.363104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21489
 
10.5%
e 16971
 
8.3%
n 14542
 
7.1%
i 14473
 
7.1%
a 14022
 
6.9%
o 13803
 
6.8%
s 13365
 
6.6%
t 10348
 
5.1%
r 9742
 
4.8%
, 7332
 
3.6%
Other values (33) 67796
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141221
69.3%
Uppercase Letter 31164
 
15.3%
Space Separator 21489
 
10.5%
Other Punctuation 9313
 
4.6%
Dash Punctuation 696
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16971
12.0%
n 14542
10.3%
i 14473
10.2%
a 14022
9.9%
o 13803
9.8%
s 13365
9.5%
t 10348
7.3%
r 9742
6.9%
m 6312
 
4.5%
l 5187
 
3.7%
Other values (10) 22456
15.9%
Uppercase Letter
ValueCountFrequency (%)
T 4955
15.9%
V 4399
14.1%
M 4249
13.6%
I 3462
11.1%
S 3365
10.8%
D 2944
9.4%
C 2807
9.0%
A 1522
 
4.9%
R 903
 
2.9%
F 854
 
2.7%
Other values (8) 1704
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 7332
78.7%
& 1635
 
17.6%
' 346
 
3.7%
Space Separator
ValueCountFrequency (%)
21489
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 172385
84.6%
Common 31498
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16971
 
9.8%
n 14542
 
8.4%
i 14473
 
8.4%
a 14022
 
8.1%
o 13803
 
8.0%
s 13365
 
7.8%
t 10348
 
6.0%
r 9742
 
5.7%
m 6312
 
3.7%
l 5187
 
3.0%
Other values (28) 53620
31.1%
Common
ValueCountFrequency (%)
21489
68.2%
, 7332
 
23.3%
& 1635
 
5.2%
- 696
 
2.2%
' 346
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21489
 
10.5%
e 16971
 
8.3%
n 14542
 
7.1%
i 14473
 
7.1%
a 14022
 
6.9%
o 13803
 
6.8%
s 13365
 
6.6%
t 10348
 
5.1%
r 9742
 
4.8%
, 7332
 
3.6%
Other values (33) 67796
33.3%

Cast
Text

MISSING 

Distinct5245
Distinct (%)96.5%
Missing530
Missing (%)8.9%
Memory size1.1 MiB
2023-12-16T05:30:05.936897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length771
Median length311
Mean length118.76899
Min length3

Characters and Unicode

Total characters645747
Distinct characters149
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5115 ?
Unique (%)94.1%

Sample

1st rowYoo Ah-in, Park Shin-hye
2nd rowHelen Mirren, Gengher Gatti
3rd rowKenya Barris, Rashida Jones, Iman Benson, Genneya Walton, Scarlet Spencer, Justin Claiborne, Ravi Cabot-Conyers
4th rowAdipati Dolken, Vanesha Prescilla, Rendi Jhon, Beby Tsabina, Denira Wiraguna, Refal Hady, Diandra Agatha, Sari Nila
5th rowAdipati Dolken, Mawar de Jongh, Sari Nila, Vonny Cornellya, Clay Gribble, Ivan Leonardy, Sarah Sechan, Jourdy Pranata
ValueCountFrequency (%)
michael 371
 
0.4%
lee 336
 
0.4%
david 325
 
0.4%
john 325
 
0.4%
kim 295
 
0.3%
james 236
 
0.3%
de 215
 
0.2%
paul 215
 
0.2%
khan 177
 
0.2%
chris 159
 
0.2%
Other values (27139) 86605
97.0%
2023-12-16T05:30:06.703036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83828
 
13.0%
a 65201
 
10.1%
e 43461
 
6.7%
i 38812
 
6.0%
n 38537
 
6.0%
, 37916
 
5.9%
r 31869
 
4.9%
o 30658
 
4.7%
l 23247
 
3.6%
h 19222
 
3.0%
Other values (139) 232996
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 430017
66.6%
Uppercase Letter 90837
 
14.1%
Space Separator 83828
 
13.0%
Other Punctuation 38836
 
6.0%
Dash Punctuation 2186
 
0.3%
Decimal Number 16
 
< 0.1%
Final Punctuation 9
 
< 0.1%
Nonspacing Mark 8
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 65201
15.2%
e 43461
10.1%
i 38812
 
9.0%
n 38537
 
9.0%
r 31869
 
7.4%
o 30658
 
7.1%
l 23247
 
5.4%
h 19222
 
4.5%
s 18572
 
4.3%
t 16665
 
3.9%
Other values (65) 103773
24.1%
Uppercase Letter
ValueCountFrequency (%)
S 8763
 
9.6%
M 8072
 
8.9%
A 7116
 
7.8%
C 5688
 
6.3%
K 5478
 
6.0%
J 5478
 
6.0%
B 5203
 
5.7%
R 4748
 
5.2%
D 4422
 
4.9%
L 4012
 
4.4%
Other values (37) 31857
35.1%
Other Punctuation
ValueCountFrequency (%)
, 37916
97.6%
. 612
 
1.6%
' 246
 
0.6%
" 33
 
0.1%
・ 22
 
0.1%
& 5
 
< 0.1%
! 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 5
31.2%
4 4
25.0%
8 3
18.8%
3 1
 
6.2%
0 1
 
6.2%
9 1
 
6.2%
6 1
 
6.2%
Nonspacing Mark
ValueCountFrequency (%)
́ 5
62.5%
️ 2
 
25.0%
้ 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 2183
99.9%
‑ 3
 
0.1%
Final Punctuation
ValueCountFrequency (%)
’ 7
77.8%
” 2
 
22.2%
Space Separator
ValueCountFrequency (%)
83828
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 520854
80.7%
Common 124885
 
19.3%
Inherited 7
 
< 0.1%
Thai 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 65201
 
12.5%
e 43461
 
8.3%
i 38812
 
7.5%
n 38537
 
7.4%
r 31869
 
6.1%
o 30658
 
5.9%
l 23247
 
4.5%
h 19222
 
3.7%
s 18572
 
3.6%
t 16665
 
3.2%
Other values (112) 194610
37.4%
Common
ValueCountFrequency (%)
83828
67.1%
, 37916
30.4%
- 2183
 
1.7%
. 612
 
0.5%
' 246
 
0.2%
" 33
 
< 0.1%
・ 22
 
< 0.1%
’ 7
 
< 0.1%
& 5
 
< 0.1%
2 5
 
< 0.1%
Other values (14) 28
 
< 0.1%
Inherited
ValueCountFrequency (%)
́ 5
71.4%
️ 2
 
28.6%
Thai
ValueCountFrequency (%)
้ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 642351
99.5%
None 3350
 
0.5%
Katakana 22
 
< 0.1%
Punctuation 13
 
< 0.1%
Diacriticals 5
 
< 0.1%
Latin Ext Additional 3
 
< 0.1%
VS 2
 
< 0.1%
Thai 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83828
 
13.1%
a 65201
 
10.2%
e 43461
 
6.8%
i 38812
 
6.0%
n 38537
 
6.0%
, 37916
 
5.9%
r 31869
 
5.0%
o 30658
 
4.8%
l 23247
 
3.6%
h 19222
 
3.0%
Other values (61) 229600
35.7%
None
ValueCountFrequency (%)
é 583
17.4%
á 476
14.2%
í 385
11.5%
ó 248
 
7.4%
ü 234
 
7.0%
ı 170
 
5.1%
ñ 115
 
3.4%
ç 104
 
3.1%
ğ 99
 
3.0%
ö 99
 
3.0%
Other values (57) 837
25.0%
Katakana
ValueCountFrequency (%)
・ 22
100.0%
Punctuation
ValueCountFrequency (%)
’ 7
53.8%
‑ 3
23.1%
” 2
 
15.4%
“ 1
 
7.7%
Diacriticals
ValueCountFrequency (%)
́ 5
100.0%
VS
ValueCountFrequency (%)
️ 2
100.0%
Latin Ext Additional
ValueCountFrequency (%)
ẹ 1
33.3%
ọ 1
33.3%
Ṣ 1
33.3%
Thai
ValueCountFrequency (%)
้ 1
100.0%

Production Country
Text

MISSING 

Distinct509
Distinct (%)9.4%
Missing559
Missing (%)9.4%
Memory size381.5 KiB
2023-12-16T05:30:07.060876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length83
Median length82
Mean length11.897929
Min length4

Characters and Unicode

Total characters64344
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)7.0%

Sample

1st rowUnited States
2nd rowSouth Korea
3rd rowItaly
4th rowUnited States
5th rowCanada
ValueCountFrequency (%)
united 2770
28.5%
states 2287
23.5%
india 669
 
6.9%
kingdom 457
 
4.7%
japan 267
 
2.7%
canada 267
 
2.7%
france 254
 
2.6%
south 230
 
2.4%
spain 190
 
2.0%
korea 189
 
1.9%
Other values (97) 2155
22.1%
2023-12-16T05:30:07.560516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 7998
12.4%
a 6760
10.5%
e 6635
10.3%
n 6121
9.5%
i 5601
 
8.7%
d 4457
 
6.9%
4330
 
6.7%
S 2798
 
4.3%
U 2786
 
4.3%
s 2686
 
4.2%
Other values (40) 14172
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49106
76.3%
Uppercase Letter 9735
 
15.1%
Space Separator 4330
 
6.7%
Other Punctuation 1173
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 7998
16.3%
a 6760
13.8%
e 6635
13.5%
n 6121
12.5%
i 5601
11.4%
d 4457
9.1%
s 2686
 
5.5%
o 1477
 
3.0%
r 1366
 
2.8%
g 1003
 
2.0%
Other values (15) 5002
10.2%
Uppercase Letter
ValueCountFrequency (%)
S 2798
28.7%
U 2786
28.6%
I 839
 
8.6%
K 739
 
7.6%
C 469
 
4.8%
J 273
 
2.8%
F 261
 
2.7%
A 252
 
2.6%
T 222
 
2.3%
N 162
 
1.7%
Other values (13) 934
 
9.6%
Space Separator
ValueCountFrequency (%)
4330
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58841
91.4%
Common 5503
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 7998
13.6%
a 6760
11.5%
e 6635
11.3%
n 6121
10.4%
i 5601
9.5%
d 4457
 
7.6%
S 2798
 
4.8%
U 2786
 
4.7%
s 2686
 
4.6%
o 1477
 
2.5%
Other values (38) 11522
19.6%
Common
ValueCountFrequency (%)
4330
78.7%
, 1173
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 7998
12.4%
a 6760
10.5%
e 6635
10.3%
n 6121
9.5%
i 5601
 
8.7%
d 4457
 
6.9%
4330
 
6.7%
S 2798
 
4.3%
U 2786
 
4.3%
s 2686
 
4.2%
Other values (40) 14172
22.0%

Release Date
Real number (ℝ)

Distinct65
Distinct (%)1.1%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2015.6439
Minimum1925
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-12-16T05:30:07.785450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1925
5-th percentile2003
Q12015
median2018
Q32019
95-th percentile2021
Maximum2021
Range96
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.257391
Coefficient of variation (CV)0.0036005324
Kurtosis20.377595
Mean2015.6439
Median Absolute Deviation (MAD)2
Skewness-3.800404
Sum12021300
Variance52.669724
MonotonicityNot monotonic
2023-12-16T05:30:07.982477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 971
16.3%
2020 946
15.9%
2018 924
15.5%
2017 661
11.1%
2016 491
8.2%
2021 416
7.0%
2015 277
 
4.6%
2014 187
 
3.1%
2013 131
 
2.2%
2012 129
 
2.2%
Other values (55) 831
13.9%
ValueCountFrequency (%)
1925 1
< 0.1%
1945 1
< 0.1%
1954 2
< 0.1%
1956 1
< 0.1%
1958 1
< 0.1%
1959 1
< 0.1%
1960 1
< 0.1%
1962 1
< 0.1%
1963 1
< 0.1%
1964 1
< 0.1%
ValueCountFrequency (%)
2021 416
7.0%
2020 946
15.9%
2019 971
16.3%
2018 924
15.5%
2017 661
11.1%
2016 491
8.2%
2015 277
 
4.6%
2014 187
 
3.1%
2013 131
 
2.2%
2012 129
 
2.2%

Rating
Categorical

Distinct11
Distinct (%)0.2%
Missing4
Missing (%)0.1%
Memory size359.0 KiB
TV-MA
2541 
TV-14
1551 
TV-PG
527 
R
385 
TV-Y
 
240
Other values (6)
719 

Length

Max length5
Median length5
Mean length4.6040584
Min length1

Characters and Unicode

Total characters27454
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTV-MA
2nd rowTV-MA
3rd rowTV-14
4th rowTV-MA
5th rowTV-14

Common Values

ValueCountFrequency (%)
TV-MA 2541
42.6%
TV-14 1551
26.0%
TV-PG 527
 
8.8%
R 385
 
6.5%
TV-Y 240
 
4.0%
PG-13 226
 
3.8%
TV-Y7 206
 
3.5%
TV-G 146
 
2.4%
PG 125
 
2.1%
G 15
 
0.3%
(Missing) 4
 
0.1%

Length

2023-12-16T05:30:08.172082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tv-ma 2541
42.6%
tv-14 1551
26.0%
tv-pg 527
 
8.8%
r 385
 
6.5%
tv-y 240
 
4.0%
pg-13 226
 
3.8%
tv-y7 206
 
3.5%
tv-g 146
 
2.4%
pg 125
 
2.1%
g 15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
- 5438
19.8%
T 5211
19.0%
V 5211
19.0%
M 2541
9.3%
A 2541
9.3%
1 1778
 
6.5%
4 1551
 
5.6%
G 1039
 
3.8%
P 878
 
3.2%
Y 446
 
1.6%
Other values (5) 820
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18254
66.5%
Dash Punctuation 5438
 
19.8%
Decimal Number 3762
 
13.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 5211
28.5%
V 5211
28.5%
M 2541
13.9%
A 2541
13.9%
G 1039
 
5.7%
P 878
 
4.8%
Y 446
 
2.4%
R 385
 
2.1%
N 1
 
< 0.1%
C 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1778
47.3%
4 1551
41.2%
3 226
 
6.0%
7 207
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 5438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18254
66.5%
Common 9200
33.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 5211
28.5%
V 5211
28.5%
M 2541
13.9%
A 2541
13.9%
G 1039
 
5.7%
P 878
 
4.8%
Y 446
 
2.4%
R 385
 
2.1%
N 1
 
< 0.1%
C 1
 
< 0.1%
Common
ValueCountFrequency (%)
- 5438
59.1%
1 1778
 
19.3%
4 1551
 
16.9%
3 226
 
2.5%
7 207
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 5438
19.8%
T 5211
19.0%
V 5211
19.0%
M 2541
9.3%
A 2541
9.3%
1 1778
 
6.5%
4 1551
 
5.6%
G 1039
 
3.8%
P 878
 
3.2%
Y 446
 
1.6%
Other values (5) 820
 
3.0%

Duration
Text

Distinct207
Distinct (%)3.5%
Missing3
Missing (%)0.1%
Memory size373.8 KiB
2023-12-16T05:30:08.585183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.1416834
Min length5

Characters and Unicode

Total characters42593
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.5%

Sample

1st row1 Season
2nd row99 min
3rd row95 min
4th row1 Season
5th row90 min
ValueCountFrequency (%)
min 3867
32.4%
1 1374
 
11.5%
season 1374
 
11.5%
seasons 723
 
6.1%
2 357
 
3.0%
3 164
 
1.4%
102 92
 
0.8%
97 91
 
0.8%
93 91
 
0.8%
94 88
 
0.7%
Other values (191) 3707
31.1%
2023-12-16T05:30:09.328105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 5964
14.0%
5964
14.0%
1 4092
9.6%
m 3867
9.1%
i 3867
9.1%
s 2820
 
6.6%
S 2097
 
4.9%
e 2097
 
4.9%
a 2097
 
4.9%
o 2097
 
4.9%
Other values (9) 7631
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22809
53.6%
Decimal Number 11723
27.5%
Space Separator 5964
 
14.0%
Uppercase Letter 2097
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4092
34.9%
9 1171
 
10.0%
2 1145
 
9.8%
0 1112
 
9.5%
8 847
 
7.2%
3 810
 
6.9%
6 693
 
5.9%
4 638
 
5.4%
7 624
 
5.3%
5 591
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
n 5964
26.1%
m 3867
17.0%
i 3867
17.0%
s 2820
12.4%
e 2097
 
9.2%
a 2097
 
9.2%
o 2097
 
9.2%
Space Separator
ValueCountFrequency (%)
5964
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 2097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24906
58.5%
Common 17687
41.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5964
33.7%
1 4092
23.1%
9 1171
 
6.6%
2 1145
 
6.5%
0 1112
 
6.3%
8 847
 
4.8%
3 810
 
4.6%
6 693
 
3.9%
4 638
 
3.6%
7 624
 
3.5%
Latin
ValueCountFrequency (%)
n 5964
23.9%
m 3867
15.5%
i 3867
15.5%
s 2820
11.3%
S 2097
 
8.4%
e 2097
 
8.4%
a 2097
 
8.4%
o 2097
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5964
14.0%
5964
14.0%
1 4092
9.6%
m 3867
9.1%
i 3867
9.1%
s 2820
 
6.6%
S 2097
 
4.9%
e 2097
 
4.9%
a 2097
 
4.9%
o 2097
 
4.9%
Other values (9) 7631
17.9%

Imdb Score
Text

MISSING 

Distinct79
Distinct (%)1.5%
Missing608
Missing (%)10.2%
Memory size348.8 KiB
2023-12-16T05:30:09.655029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters32154
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row6.6/10
2nd row6.2/10
3rd row6.4/10
4th row6.6/10
5th row5.1/10
ValueCountFrequency (%)
7.1/10 233
 
4.3%
6.6/10 203
 
3.8%
6.4/10 200
 
3.7%
7.3/10 193
 
3.6%
6.5/10 191
 
3.6%
7.0/10 186
 
3.5%
7.4/10 184
 
3.4%
7.2/10 181
 
3.4%
6.7/10 176
 
3.3%
7.5/10 169
 
3.2%
Other values (69) 3443
64.2%
2023-12-16T05:30:10.126512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5929
18.4%
1 5921
18.4%
. 5359
16.7%
/ 5359
16.7%
6 2226
 
6.9%
7 2110
 
6.6%
5 1565
 
4.9%
8 1045
 
3.2%
4 960
 
3.0%
3 675
 
2.1%
Other values (2) 1005
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21436
66.7%
Other Punctuation 10718
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5929
27.7%
1 5921
27.6%
6 2226
 
10.4%
7 2110
 
9.8%
5 1565
 
7.3%
8 1045
 
4.9%
4 960
 
4.5%
3 675
 
3.1%
2 511
 
2.4%
9 494
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 5359
50.0%
/ 5359
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5929
18.4%
1 5921
18.4%
. 5359
16.7%
/ 5359
16.7%
6 2226
 
6.9%
7 2110
 
6.6%
5 1565
 
4.9%
8 1045
 
3.2%
4 960
 
3.0%
3 675
 
2.1%
Other values (2) 1005
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5929
18.4%
1 5921
18.4%
. 5359
16.7%
/ 5359
16.7%
6 2226
 
6.9%
7 2110
 
6.6%
5 1565
 
4.9%
8 1045
 
3.2%
4 960
 
3.0%
3 675
 
2.1%
Other values (2) 1005
 
3.1%

Content Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size365.5 KiB
Movie
3867 
TV Show
2100 

Length

Max length7
Median length5
Mean length5.7038713
Min length5

Characters and Unicode

Total characters34035
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTV Show
2nd rowMovie
3rd rowMovie
4th rowTV Show
5th rowMovie

Common Values

ValueCountFrequency (%)
Movie 3867
64.8%
TV Show 2100
35.2%

Length

2023-12-16T05:30:10.296923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T05:30:10.493244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
movie 3867
47.9%
tv 2100
26.0%
show 2100
26.0%

Most occurring characters

ValueCountFrequency (%)
o 5967
17.5%
M 3867
11.4%
v 3867
11.4%
i 3867
11.4%
e 3867
11.4%
T 2100
 
6.2%
V 2100
 
6.2%
2100
 
6.2%
S 2100
 
6.2%
h 2100
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21768
64.0%
Uppercase Letter 10167
29.9%
Space Separator 2100
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5967
27.4%
v 3867
17.8%
i 3867
17.8%
e 3867
17.8%
h 2100
 
9.6%
w 2100
 
9.6%
Uppercase Letter
ValueCountFrequency (%)
M 3867
38.0%
T 2100
20.7%
V 2100
20.7%
S 2100
20.7%
Space Separator
ValueCountFrequency (%)
2100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31935
93.8%
Common 2100
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5967
18.7%
M 3867
12.1%
v 3867
12.1%
i 3867
12.1%
e 3867
12.1%
T 2100
 
6.6%
V 2100
 
6.6%
S 2100
 
6.6%
h 2100
 
6.6%
w 2100
 
6.6%
Common
ValueCountFrequency (%)
2100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 5967
17.5%
M 3867
11.4%
v 3867
11.4%
i 3867
11.4%
e 3867
11.4%
T 2100
 
6.2%
V 2100
 
6.2%
2100
 
6.2%
S 2100
 
6.2%
h 2100
 
6.2%

Date Added
Date

MISSING 

Distinct1283
Distinct (%)27.7%
Missing1335
Missing (%)22.4%
Memory size46.7 KiB
Minimum2008-01-01 00:00:00
Maximum2021-07-16 00:00:00
2023-12-16T05:30:10.713348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-16T05:30:10.900611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-16T05:29:58.702462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-12-16T05:30:11.040540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Release DateRatingContent Type
Release Date1.0000.1020.153
Rating0.1021.0000.310
Content Type0.1530.3101.000

Missing values

2023-12-16T05:29:58.930650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T05:29:59.280318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-16T05:29:59.609461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Show IdTitleDescriptionDirectorGenresCastProduction CountryRelease DateRatingDurationImdb ScoreContent TypeDate Added
0cc1b6ed9-cf9e-4057-8303-34577fb54477(Un)WellThis docuseries takes a deep dive into the lucrative wellness industry, which touts health and healing. But do the products live up to the promises?NaNReality TVNaNUnited States2020.0TV-MA1 Season6.6/10TV ShowNaN
1e2ef4e91-fb25-42ab-b485-be8e3b23dedb#AliveAs a grisly virus rampages a city, a lone man stays locked inside his apartment, digitally cut off from seeking help and desperate to find a way out.Cho IlHorror Movies, International Movies, ThrillersYoo Ah-in, Park Shin-hyeSouth Korea2020.0TV-MA99 min6.2/10MovieSeptember 8, 2020
2b01b73b7-81f6-47a7-86d8-acb63080d525#AnneFrank - Parallel StoriesThrough her diary, Anne Frank's story is retold alongside those of five Holocaust survivors in this poignant documentary from Oscar winner Helen Mirren.Sabina Fedeli, Anna MigottoDocumentaries, International MoviesHelen Mirren, Gengher GattiItaly2019.0TV-1495 min6.4/10MovieJuly 1, 2020
3b6611af0-f53c-4a08-9ffa-9716dc57eb9c#blackAFKenya Barris and his family navigate relationships, race and culture while grappling with their newfound success in this comedy series.NaNTV ComediesKenya Barris, Rashida Jones, Iman Benson, Genneya Walton, Scarlet Spencer, Justin Claiborne, Ravi Cabot-ConyersUnited States2020.0TV-MA1 Season6.6/10TV ShowNaN
47f2d4170-bab8-4d75-adc2-197f7124c070#cats_the_mewvieThis pawesome documentary explores how our feline friends became online icons, from the earliest text memes to the rise of celebrity cat influencers.Michael MargolisDocumentaries, International MoviesNaNCanada2020.0TV-1490 min5.1/10MovieFebruary 5, 2020
5c293788a-41f7-49a3-a7fc-005ea33bce2b#FriendButMarriedPining for his high school crush for years, a young man puts up his best efforts to move out of the friend zone until she reveals she's getting married.Rako PrijantoDramas, International Movies, Romantic MoviesAdipati Dolken, Vanesha Prescilla, Rendi Jhon, Beby Tsabina, Denira Wiraguna, Refal Hady, Diandra Agatha, Sari NilaIndonesia2018.0TV-G102 min7.0/10MovieMay 21, 2020
60555e67e-f624-4a05-93e4-55c117d0056d#FriendButMarried 2As Ayu and Ditto finally transition from best friends to newlyweds, a quick pregnancy creates uncertainty for the future of their young marriage.Rako PrijantoDramas, International Movies, Romantic MoviesAdipati Dolken, Mawar de Jongh, Sari Nila, Vonny Cornellya, Clay Gribble, Ivan Leonardy, Sarah Sechan, Jourdy PranataIndonesia2020.0TV-G104 min7.0/10MovieJune 28, 2020
7c844460f-6178-4f87-929e-80816c74ca35#realityhighWhen nerdy high schooler Dani finally attracts the interest of her longtime crush, she lands in the cross hairs of his ex, a social media celebrity.Fernando LebrijaComediesNesta Cooper, Kate Walsh, John Michael Higgins, Keith Powers, Alicia Sanz, Jake Borelli, Kid Ink, Yousef Erakat, Rebekah Graf, Anne Winters, Peter Gilroy, Patrick DavisUnited States2017.0TV-1499 min5.1/10MovieSeptember 8, 2017
88b34e0e9-7258-4e49-b799-2e7eddbd7e34#Rucker50This documentary celebrates the 50th anniversary of the Harlem sports program that has inspired countless city kids to become pro basketball players.Robert McCullough Jr.Documentaries, Sports MoviesNaNUnited States2016.0TV-PG56 min5.1/10MovieDecember 1, 2016
96da2fc83-1546-4e9d-bf2e-9b472a059c18#SelfieTwo days before their final exams, three teen girls make a seaside getaway to have the time of their lives.Cristina JacobComedies, Dramas, International MoviesFlavia Hojda, Crina Semciuc, Olimpia Melinte, Sali Levent, Vlad Logigan, Alex Călin, Alina Chivulescu, Răzvan VasilescuRomania2014.0TV-MA125 min5.8/10MovieJune 21, 2021
Show IdTitleDescriptionDirectorGenresCastProduction CountryRelease DateRatingDurationImdb ScoreContent TypeDate Added
5957309b0d7e-357b-45ce-b1cf-617b4c3593d5Zoot SuitIn this drama based on a 1940s trial, the members of a Mexican American gang are sentenced to San Quentin for a murder they may not have committed.Luis ValdezCult Movies, Dramas, Music & MusicalsDaniel Valdez, Edward James Olmos, Charles Aidman, Tyne Daly, John Anderson, Abel Franco, Mike Gomez, Francis X. McCarthy, Alma Martínez, Lupe Ontiveros, Tony PlanaUnited States1981.0R103 min7.0/10MovieApril 15, 2021
5958a006d6c6-e213-4a8b-9f57-b941e822d0c5ZozoWhen Lebanon's Civil War deprives Zozo of his family, he's left with grief and little means as he escapes to Sweden in search of his grandparents.Josef FaresDramas, International MoviesImad Creidi, Antoinette Turk, Elias Gergi, Carmen Lebbos, Viktor Axelsson, Charbel Iskandar, Yasmine AwadSweden, Czech Republic, United Kingdom, Denmark, Netherlands2005.0TV-MA99 min6.7/10MovieOctober 19, 2020
595929166831-eb84-4a0d-b2e0-e4c13fea7c6dZulu Man in JapanIn this documentary, South African rapper Nasty C hits the stage and streets of Tokyo, introducing himself to the city's sights, sounds and culture.NaNDocumentaries, International Movies, Music & MusicalsNasty CNaN2019.0TV-MA44 minNaNMovieSeptember 25, 2020
5960a86d832c-1082-4b10-82a3-766bb539e3aaZumbo's Just DessertsDessert wizard Adriano Zumbo looks for the next “Willy Wonka” in this tense competition that finds skilled amateurs competing for a $100,000 prize.NaNInternational TV Shows, Reality TVAdriano Zumbo, Rachel KhooAustralia2019.0TV-PG1 Season7.1/10TV ShowOctober 31, 2020
596106aab7f2-9756-4680-98cf-209b7ca86a9bZZ TOP: THAT LITTLE OL' BAND FROM TEXASThis documentary delves into the mystique behind the blues-rock trio and explores how the enigmatic band created their iconic look and sound.Sam DunnDocumentaries, Music & MusicalsNaNUnited Kingdom, Canada, United States2019.0TV-MA90 min7.4/10MovieMarch 1, 2020
596262b8b682-f191-4c10-aa04-32319329bd8dالف مبروكOn his wedding day, an arrogant, greedy accountant experiences a series of calamities that keep replaying as he lives the same day over and over again.Ahmed Nader GalalComedies, Dramas, International MoviesAhmed Helmy, Laila Ezz El Arab, Mahmoud El Fishawy, Mohamed Farraag, Sarrah Abdelrahman, Rahma Hassan, Amir Salah, Hany El SabaghEgypt2009.0TV-14115 min7.4/10MovieApril 25, 2020
59635bed77ab-5e31-4216-8b51-44c9a35442e6دفعة القاهرةA group of women leaves Kuwait to attend university in Cairo, embarking on personal journeys filled with romance and self-discovery.NaNInternational TV Shows, TV DramasBashar al-Shatti, Fatima Al Safi, Maram Balochi, Hamad Ashkanani, Nour Alghandour, Khaled Al-ShaerNaN2019.0TV-141 SeasonNaNTV ShowNaN
59644661ec0c-8692-4661-bc76-a96412b311fd海的儿子Two brothers start a new life in Singapore, where they run into a childhood friend who falls in love with the elder while attracting the younger.NaNInternational TV Shows, TV DramasLi Nanxing, Christopher Lee, Jesseca Liu, Apple Hong, Jeanette AwNaN2016.0TV-141 SeasonNaNTV ShowNaN
5965145c93a7-1924-403c-a933-4ede8ad66f26반드시 잡는다After people in his town start turning up dead, a grumpy landlord is visited by a man who recounts an unsolved serial murder case from 30 years ago.Hong-seon KimDramas, International Movies, ThrillersBaek Yoon-sikSouth Korea2017.0TV-MA110 min6.5/10MovieFebruary 28, 2018
5966d4613d34-cb71-4bd6-b570-fa857f02c44d최강전사 미니특공대 : 영웅의 탄생Miniforce, a special task force of elite rangers, takes on the Lizard Army to save Earth before it’s too late, in this prequel to the TV series.Young Jun LeeChildren & Family MoviesUm Sang-hyun, Yang Jeong-hwa, Jeon Tae-yeol, Shin Yong-woo, Lee So-young, So-yeonNaN2018.0TV-Y768 min4.7/10MovieSeptember 1, 2018